Olaparib adaptation continuum: single-cell trajectory reanalysis

Reproducible reanalysis of single-cell RNA-seq data from olaparib-adapted Kuramochi ovarian cancer cells. The project reconstructs the adaptive continuum with Monocle 3, evaluates gene–pseudotime associations with PseudotimeDE, compares them with principal-graph autocorrelation, classifies dynamic expression patterns, and performs GO Biological Process and Reactome enrichment.

Main findings

• 7,751 G1-phase cells were retained for the primary trajectory.
• Pseudotime recovered the published continuum from control-associated states toward advanced olaparib adaptation.
• PseudotimeDE identified 7,115 trajectory-associated genes.
• 6,253 genes were supported by both PseudotimeDE and Monocle 3 graph_test within the common tested universe (Jaccard index = 0.868).
• After dynamic-range filtering, 6,883 genes were assigned to decreasing, early, transient, increasing, or late expression patterns.
• Functional reprogramming shifted from RNA processing and intracellular trafficking toward mitochondrial translation, respiration, ribosome biogenesis, and autophagy.

Official repository

This project is maintained in the CNPEM GitHub organization:

• https://github.com/cnpem/olaparib-reconstruction

Repository structure

.
├── scripts/
│   ├── 00_install_dependencies.R
│   ├── 00_check_environment.R
│   ├── 01_trajectory_inference.R
│   ├── 02_pseudotimeDE.R
│   ├── 03_dynamic_transcriptional_reconstruction.R
│   └── slurm/
├── config/example.env
├── data/README.md
├── results/
│   ├── summary/
│   └── enrichment/
├── figures/
├── manuscript/
├── environment/
└── docs/

Data

The input dataset is publicly available from GEO under accession GSE206125. Raw data are intentionally excluded from this repository. See data/README.md for expected filenames and configuration.

Reproducibility

The reported analysis used R 4.5.2 with Seurat 5.5.0, Monocle 3 1.4.27, PseudotimeDE 0.99.0, clusterProfiler 4.18.4, ReactomePA 1.54.0, and org.Hs.eg.db 3.22.0. Complete sessionInfo() records are stored in environment/.

1. Install and verify packages

Rscript scripts/00_install_dependencies.R
Rscript scripts/00_check_environment.R

2. Configure the project

cp config/example.env .env
source .env

3. Run Stage 1 interactively

Root selection uses the Monocle 3 graphical interface and must therefore be performed in an interactive R session:

Sys.setenv(OLAPARIB_BASE_DIR = normalizePath("."))
source("scripts/01_trajectory_inference.R")

4. Run PseudotimeDE

Local execution:

Rscript scripts/02_pseudotimeDE.R "$(pwd)"

SLURM execution:

mkdir -p logs
sbatch scripts/slurm/02_pseudotimeDE.slurm.sh "$(pwd)"

5. Run dynamic reconstruction and enrichment

Rscript scripts/03_dynamic_transcriptional_reconstruction.R "$(pwd)"

Or submit Stages 2 and 3 with a dependency:

bash scripts/slurm/submit_stages_2_and_3.sh "$(pwd)"

Figures

Trajectory reconstruction	Dynamic programs	Functional reprogramming

Manuscript

The BSBS/SBC LaTeX manuscript is in manuscript/. Add the official sbc-template.sty and sbc.bst files to that directory, then run:

cd manuscript
latexmk -pdf main.tex

Citation

Citation metadata are provided in CITATION.cff. The repository also includes .zenodo.json to simplify archival release and DOI generation.

License

A software license has not yet been selected. Until a license is added, reuse requires permission from the authors. Select a license before making the repository public.

Authors

• Maurício Frota Camacho — LNBio/CNPEM
• Nilson Coimbra — LNBio/CNPEM
• Adriana Franco Paes Leme — LNBio/CNPEM

Source study

França GS, Baron M, King BR, et al. Cellular adaptation to cancer therapy along a resistance continuum. Nature. 2024;631:876–883. https://doi.org/10.1038/s41586-024-07690-9